




'''simple docstring'''







from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   return {key.lstrip('-'						): value for key, value in zip(unknown_args[::2]      ,unknown_args[1::2]						)}







def 						_A      ( ):






   '''simple docstring'''







   A__       =     ArgumentParser(
       'HuggingFace Datasets CLI tool'      ,usage='datasets-cli <command> [<args>]'      ,allow_abbrev=UpperCAmelCase						)
   A__       =     parser.add_subparsers(help='datasets-cli command helpers'						)
   set_verbosity_info()

   # Register commands
   ConvertCommand.register_subcommand(UpperCAmelCase						)
   EnvironmentCommand.register_subcommand(UpperCAmelCase						)
   TestCommand.register_subcommand(UpperCAmelCase						)
   RunBeamCommand.register_subcommand(UpperCAmelCase						)
   DummyDataCommand.register_subcommand(UpperCAmelCase						)

   # Parse args
   A__  ,			A__       =     parser.parse_known_args()
   if not hasattr(UpperCAmelCase      ,'func'						):
      parser.print_help()
      exit(1						)
   A__       =     parse_unknown_args(UpperCAmelCase						)

   # Run
   A__       =     args.func(UpperCAmelCase      ,**UpperCAmelCase						)
   service.run()


if __name__ == "__main__":
    main()









"""simple docstring"""





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
       import torch

       from transformers import AutoModelForImageClassification

if is_vision_available():
       from transformers import AutoImageProcessor

@require_torch
@require_vision
class 						lowercase   (   unittest.TestCase   ):





      @slow
      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""					)
            lowerCAmelCase    		=			AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""					)
            model.to(lowercase					)

            from datasets import load_dataset

            lowerCAmelCase    		=			load_dataset("""nielsr/rvlcdip-demo"""					)

            lowerCAmelCase    		=			dataset["""train"""][0]["""image"""].convert("""RGB"""					)

            lowerCAmelCase    		=			image_processor(lowercase ,		return_tensors="""pt"""					).to(lowercase					)

            # forward pass
            with torch.no_grad():
                  lowerCAmelCase    		=			model(**lowercase					)
                  lowerCAmelCase    		=			outputs.logits

            lowerCAmelCase    		=			torch.Size((1, 16)					)
            self.assertEqual(logits.shape ,		lowercase					)

            lowerCAmelCase    		=			torch.tensor(
                [-0.4_158, -0.4_092, -0.4_347] ,		device=lowercase ,		dtype=torch.float ,		)
            self.assertTrue(torch.allclose(logits[0, :3] ,		lowercase ,		atol=1e-4					)					)






